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Found 4 papers, 2 papers with code
Benign overfitting in leaky ReLU networks with moderate input dimension
The problem of benign overfitting asks whether it is possible for a model to perfectly fit noisy training data and still generalize well.
Mildly Overparameterized ReLU Networks Have a Favorable Loss Landscape
We study the loss landscape of both shallow and deep, mildly overparameterized ReLU neural networks on a generic finite input dataset for the squared error loss.
FoSR: First-order spectral rewiring for addressing oversquashing in GNNs
On the other hand, adding edges to the message-passing graph can lead to increasingly similar node representations and a problem known as oversmoothing.
Oversquashing in GNNs through the lens of information contraction and graph expansion
We compare the spectral expansion properties of our algorithm with that of an existing curvature-based non-local rewiring strategy.
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